CausalImpact is an R package developed by Google for causal inference using Bayesian Structural time-series models. You can find the R version here.
In short, what this package does is making counterfactual predictions. In other words, what would have happened in a parallel (sort of) universe if an intervention never had happened? Here is a quick example straight from Google’s website: “Given a response time series (e.g., clicks) and a set of control time series (e.g., clicks in non-affected markets or clicks on other sites), the package constructs a Bayesian structural time-series model. This model is then used to try and predict the counterfactual, i.e., how the response metric would have evolved after the intervention if the intervention had never occurred.”
CausalImpact 1.2.1, Brodersen et al., Annals of Applied Statistics (2015). http://google.github.io/CausalImpact/
Image Description: Part A of the image (original) shows, with the dark continuous line, the time series of something we are monitoring. The blue dotted one is the counterfactual prediction. The vertical grey line is the moment when an intervention was made. We can observe that from that moment onwards, blue and black lines drift apart. Part B (pointwise) illustrates the difference of those lines over time which in essence is the causal effect we are interested in, while Part C (cumulative) is the cumulative difference over time.
I know you can work with R, but for Python lovers, I am not aware of the equivalent package. Surely, there are some libraries implementing parts of the original paper. By checking out some of those Python implementations I noticed differences in terms of the results. Long story short, here you can check how to run this package from python. Similarly, the approach is generalisable to probably any R package for that matter.
What worked for me was to create a new Conda environment with both Python libraries and core R packages pre-installed. Here is an example:
conda create -n r_env numpy pandas statsmodels r-essentials r-base
Creating the environment should take some time. Also, note that Jupyter notebook requires further configuration so I tend to edit the code in any programming editor instead and run from the command line.
What we would also need is
rpy2 which does all the work for us. It is a python interface to the R language.
pip install rpy2 would do.
Load all the libraries as below:
#rpy2 lib from rpy2.robjects.packages import importr import rpy2.robjects as robjects from rpy2.robjects import pandas2ri from rpy2.robjects import Formula import rpy2.robjects.packages as rpackages import rpy2.robjects.vectors as StrVector from rpy2.ipython.ggplot import image_png #typical python libs import numpy as np import pandas as pd import datetime #arma from statsmodels.tsa.arima_process import ArmaProcess
#python #timeseries #causality #r #counterfactual
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
March 25, 2021 Deepak@321 0 Comments
Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
#python #packages or libraries #python 20 modules #python 20 most usefull modules #python intersting modules #top 20 python libraries #top 20 python modules #top 20 python packages
I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.
I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.
Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).
Therefore, the short answer on whether you should learn Python or R is: it depends.
The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.
#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r
Module: It is a simple Python file that contains collections of functions and global variables and has a “.py” extension file. It’s an executable file and we have something called a “Package” in Python to organize all these modules.
Package: It is a simple directory which has collections of modules, i.e., a package is a directory of Python modules containing an additional init.py file. It is the init.py which maintains the distinction between a package and a directory that contains a bunch of Python scripts. A Package simply is a namespace. A package can also contain sub-packages.
When we import a module or a package, Python creates a corresponding object which is always of type module . This means that the dissimilarity is just at the file system level between module and package.
#technology #python #what's the difference between a python module and a python package? #python package #python module